Fusion of inertial and visual information for indoor localisation. Issue 13 (1st June 2018)

• Record Type: Journal Article
• Authors: Xu, Yan; Yu, Hang; Zhang, Jiahe
• Is Part Of: Electronics letters. Volume 54:Issue 13(2018)
• Page Start: 850
• Page End: 851
• Publication Date: 2018-06-01
• Subjects: sensor fusion -- inference mechanisms -- indoor radio -- learning (artificial intelligence) -- uncertainty handling -- inertial navigation -- indoor navigation -- location based services; inertial information -- visual information -- indoor localisation -- location‐based services -- fusion algorithm -- YELM‐DS -- extreme learning machine -- ELM -- input data model -- target output positions -- Dempster–Shafer evidence theory -- D–S evidence theory -- trust degree; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0366
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Indoor localisation has attracted a lot of attention because of its importance for location‐based services. A fusion algorithm (named as YELM‐DS) based on extreme learning machine (ELM) and Dempster–Shafer (D–S) evidence theory is proposed. ELM learns the input data model composed of inertial and visual information and target output positions with high speed. During online phase, the final localisation result of a frame is decided by the trust degree obtained from D–S. Angle judgments are also introduced to decrease the big localisation errors of turning. Compared with the existing vision‐only methods, the proposed method can both run in real time and achieve good localisation accuracy even in challenging scenarios.